Medical ultrasonic imaging system with adaptive multi-dimensional back-end mapping

ABSTRACT

A medical ultrasonic imaging system uses an adaptive multi-dimensional back-end mapping stage to eliminate loss of information in the back-end, minimize any back-end quantization noise, reduce or eliminate electronic noise, and map the local average of soft tissue to a target display value throughout the image. The system uses spatial variance to identify regions of the image corresponding substantially to soft tissue and a noise frame acquired with the transmitters turned off to determine the mean system noise level. The system then uses the mean noise level and the identified regions of soft tissue to both locally and adaptively set various back-end mapping stages, including the gain and dynamic range.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of U.S. Pat. No. 6,398,733Ser. No. 04/556,354, filed Apr. 24, 2000, the entirety of which ishereby incorporated by reference.

BACKGROUND

The present invention relates to medical ultrasonic imaging, and inparticular to systems that adaptively set one or more stages of back-endmapping that may include gain, dynamic range and post-processing mapstages in one or more image dimensions to improve such imaging.

In conventional ultrasonic imaging, a B-mode signal is adjusted for gainand dynamic range before it is mapped to a range of gray levels orcolors for display. The dynamic range of the signal to be displayed canconventionally be set by the user by means of a display dynamic rangecontrol. This control is conventionally independent of range andazimuthal position in the image. The gain can conventionally be variedby the user using a depth gain compensation (DGC) or a time gaincompensation (TGC) control along with the master gain or B gain control.The DGC and TGC controls are conventionally variable in range (axialdimension) only, and the master gain is independent of both range andlateral (azimuthal) position. A few systems also offer lateral gaincompensation in addition to depth gain compensation, but the twoone-dimensional gain controls comprise only an approximation to a truetwo-dimensional gain control.

After gain and display dynamic range have been applied, log-compressedB-mode signals are re-quantized, typically to 8-bit or 256 quantizationlevels. The quantization step (in dB) is given by the ratio of thedynamic range selected by the user to the number of quantization levels.After quantization, a post-processing map is used to map thequantization levels to a range of gray levels or colors. This map can bea selected one of a predesigned set of maps or alternately auser-designed map. These maps are also conventionally range and azimuthindependent.

On commercially available ultrasound imaging systems, gain controls areoften used by the users to adjust the brightness level. In many cases,users adjust the gain mainly to keep the regional mean of the softtissue gray level within a narrow range of gray values across the image.This preferred range is consistent from user to user, and in many casesusers tend to adjust the gain to set the gray level for soft tissueroughly to the 64th gray level on a linear map that maps 0 to black and255 to white. However, gain adjustments for soft tissue brightness leveland uniformity do not simultaneously optimize noise suppression andprevent display saturation. For this reason, gain and/or dynamic rangeare frequently sub-optimal for some or all parts of an image. As aresult, information can be lost by cutting off low-level signals orsaturating high-level signals.

Such loss of information due to errors in setting gain and/or dynamicrange can be reduced or eliminated by setting the dynamic range to avery high level. This approach however reduces contrast resolutionbecause different tissue types are then mapped to similar gray levels,thereby reducing the prominence of echogenicity differences.

U.S. Pat. No. 5,579,768 to Klesenski (assigned to the assignee of thepresent invention) proposes an automatic gain compensation system thatuses B-mode intensity of image signals to identify regions of softtissue, and then automatically sets these regions of soft tissue to apredetermined magnitude.

U.S. Pat. No. 5,993,392 to Roundhill discloses an ultrasonic imagingsystem in which dynamic range is selected based upon the range andazimuthal position of the image signal within the frame. The disclosedsystem is not responsive to the image signal itself, and thereforecannot be considered to be an adaptive system. Rather, the approach usedin the Roundhill patent is to select a stored compression map as afunction of the range and azimuth of the display signal.

SUMMARY

Conventional ultrasound imaging systems use various control stages inthe backend to map a range (window) of input signal levels to a range ofdisplay gray levels or colors. These stages may include a single ormultiple gain stages, a dynamic range control stage, post processingmaps, etc. The dynamic range control allows users to adjust the width ofthe window of input signal levels to be displayed. We will refer to thisuser control as the display dynamic range control to differentiate itfrom other possible windowing operations a system might have. The gaincontrols let the user adjust the position of this window. Therefore, thedynamic range and gain stages together determine the actual window ofinput signal levels to be displayed without saturation. Apost-processing map then determines the actual gray levels and or colorsthat correspond to the signal levels thus selected for display.

Ideally, the display dynamic range should be set equal to the dynamicrange of the input signal, and the gain should be set to match the fullrange of input signal to the full range of displayed values. In thisway, no signal is lost and the back-end quantization noise is minimized.In addition, the regional mean of the soft tissue signal should bemapped to a particular display level (e.g., gray level) uniformly acrossthe image for display uniformity.

The dynamic range of a B-mode signal is determined by the noise level ofthe system and the maximum echo level. The noise level of the system isrange and azimuth dependent, due to range and azimuth dependentfront-end gain and imager aperture size. The maximum echo level isdetermined by the transmit field strength, the attenuation of themedium, the reflectivity of the object being observed, and the coherentgain of the receive beamformer. For these reasons, an adaptive andmulti-dimensional back-end mapping stage or stages are required toachieve the above-mentioned mapping goals.

We here describe an adaptive and multi-dimensional method that a)prevents loss of information in the back-end, b) reduces or eliminateselectronic noise in the displayed images, c) minimizes the back-endquantization noise and d) for B-mode, maps the regional mean of softtissue to a programmable target display level for tissue. We alsodescribe several reduced implementations that satisfy a subset of theabove list. The reduced implementations adaptively adjust gain and insome cases dynamic range in two dimensions to display imagessubstantially free of electronic noise and to display tissue at a targettissue gray level.

Note that the term “input signal” is used broadly to refer to amplitude,intensity or log-compressed amplitude of the beamformer output (i.e.B-mode signal) as well as to any parameter of interest derived orextracted from the beamformer output, including the average velocity andpower estimates of the Doppler frequency shift (i.e. Color Doppler Modesignals) and the power spectrum estimate of the Doppler frequency shift(i.e., Spectral Doppler Mode signals). The foregoing paragraphs havebeen provided by way of introduction, and they are not intended to limitthe scope of the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a medical diagnostic ultrasonic imagingsystem that incorporates a preferred embodiment of this invention.

FIG. 2 is a block diagram of a first preferred embodiment of themulti-dimensional back-end mapping stage of FIG. 1.

FIG. 3 is a block diagram of a modification to the embodiment of FIG. 2.

FIGS. 4, 5, and 6 are graphs used to illustrate alternative mappingfunctions performed by the embodiment of FIG. 2.

FIG. 7 is a flow chart of a method performed by the embodiment of FIG.2.

FIG. 8 is a block diagram of another embodiment of the adaptivemultidimensional back-end mapping stage of FIG. 1.

FIG. 9 is a more detailed block diagram of a first preferred embodimentof the mapping stage of FIG. 8.

FIG. 10 is a flowchart of a method implemented by the embodiment of FIG.9.

FIGS. 11, 12 and 13 are graphs illustrating operation of the embodimentof FIG. 9.

FIG. 14 is a block diagram of a second preferred embodiment of the gainprocessor of FIG. 8, which operates to set both local gain and localdynamic range adaptively.

FIG. 15 is a block diagram of a third embodiment of the gain processorof FIG. 8, which operates to set both the local gain and the localdynamic range adaptively.

FIG. 16 is a graph used to explain operation of the embodiment of FIG.15.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS

Turning now to the drawings, FIG. 1 is a block diagram of a medicaldiagnostic ultrasonic imaging system 10 that incorporates a preferred itembodiment of this invention. As shown in FIG. 1, a transmit beamformer11 applies transmit waveforms via a transmit/receive switch 12 to atransducer array 13. The transducer array 13 produces ultrasonic pulsesin response to the transmit waveforms, which pulses are directed into abody B to be imaged. Returning echoes from the body B impinge upon thetransducer array 13, which converts these echoes into receive signalsthat are transmitted via the transmit/switch 12 to a receive beamformer14. The receive beamformer 14 applies appropriate delays and phaseshifts to cause the receive signals from selected locations within thebody B to add coherently. These beamformed signals are applied to anamplitude detector 15 and a back-end processor that includes a logcompression device 16 before being applied to a scan converter 17. Thescan converter 17 generates display values upon a grid appropriate for adisplay 19.

All of the elements 11-17 and 19 can take any suitable form, and are notlimited to any particular implementation. For example, the transmit andreceive beamformers can be constructed as analog or digital devices, andany suitable transducer array can be used, including a single-elementtransducer array and phased arrays of various dimensions. Also, thesystem 10 may include additional elements in the signal path between thetransducer array 13 and the display 19, and selected ones of theillustrated elements may be deleted or the order of some of the elementsmay be switched. For example the order of the back-end processor andscan converter 17 can be altered.

The back-end processor also includes an adaptive multi-dimensionalback-end mapping stage 18 that incorporates a preferred embodiment ofthis invention. This mapping stage 18 can take many forms, and fourspecific embodiments are described below.

First Preferred Embodiment

FIG. 2 shows a block diagram of a general embodiment of the mappingstage 18. The embodiment of FIG. 2 receives an input signal I(x)generated by the log compression device 16. Simply by way of example,the input signal can be a B-mode image signal.

The input signal I(x) and a local noise mean estimate generated by anestimator 20 are applied to a summer 24. The local noise mean estimator20 estimates the local noise of the system as a function of positionwithin an image. As explained in greater detail below, severalapproaches can be used *to estimate the local noise mean. For example,one or more frames of image data may be acquired without applyingtransmit signals to the transducer elements of the transducer 13. In theabsence of an insonifying pressure wave, the resulting input signalforms a noise frame that is a measure of currently-prevailing systemnoise as a function of position within the image. This noise frame canthen be filtered with a low pass filter in the estimator 20 in order togenerate the local noise mean {overscore (N)}(x). This parameter issubtracted from the input signal I(x) in the summer 24. The output ofthe summer 24 represents a noise suppressed input signal I_(n)(x), whichis applied in parallel to a tissue mean estimator 21, a maximum SNRestimator 25 and an adaptive multi-dimensional mapping stage 26.

The tissue mean estimator 21 processes the noise suppressed input signalI_(n)(x) to develop an output signal I_(t)(x) that is indicative of thelocal mean of I_(n)(x) for those portions of I_(n)(x) acquired from softtissue.

The tissue mean estimator 21 includes a tissue detector 22 and a meanestimator 23. The tissue detector identifies those portions of I_(n)(x)characteristic of soft tissue and generates an output signal T(x) whichis in the logic state 1 for values of x associated with soft tissue andis in the logic state 0 for values of x not associated with soft tissue.The tissue detector 22 can take many forms, and it can operate bycomparing the variance of I_(n)(x) with a target value characteristic ofsoft tissue, as described in greater detail below. Alternatively, thetissue detector 22 can use amplitude techniques to detect soft tissue,as described in Klesenski U.S. Pat. No. 5,579,768. The tissue detector22 implements Eq. 1:

R(x ₀)={T(x)=1}∩{|x−x ₀ ≦W _(t)}  (Eq. 1)

In Eq. 1 W_(t) is the width array that defines the region R around x₀.The mean estimator 22 uses I_(n)(x) and T(x) to implement Eq. 2:

{overscore (I)} _(t)(x ₀)=<I _(n)(x)>_(xεR)(x ₀)  (Eq. 2)

In Eq. 2 the symbol <•> is the mean operator over x within the region Rwhich is a function of the position x₀.

If R(x₀), is empty for a given W_(t) (i.e. there is no soft tissuewithin the distance W_(t) around x₀), then we either increase W_(t)until R is not empty or interpolate/extrapolate {overscore (I)}_(t)(x)around x₀ to determine {overscore (I)}_(t)(x₀). Note that W_(t) can bedependent upon (x₀) or not. For example W_(t)(x₀) can be set as afunction of the lateral and axial resolution at (x₀).

In an alternative embodiment, T(x) can be a tissue map which indicatesthe likelihood that the image at position x was acquired from softtissue, wherein the map assumes intermediate values between 0 and 1, andincreasing values for the tissue map correspond to increasing levels oflikelihood that the associated input signal was acquired from softtissue. In this case, T(x) can be used as a weight in computing{overscore (I)}_(t)(x). In addition T(x) can also be superimposed on aB-Mode image to enhance tissue differences.

The maximum SNR estimator 25 implements Eq. 3: $\begin{matrix}{{I_{m}\left( x_{0} \right)} = {\max\limits_{{{x - x_{0}}} \leq w_{m}}\left\{ {I_{n}(x)} \right\}}} & \left( {{Eq}.\quad 3} \right)\end{matrix}$

In Eq. 3, the function MAX{•} is the maximum operator; W_(m) is thewidth array that defines the region R around x₀. If desired, W_(m) canbe set equal to W_(t) described above. The maximum SNR estimator 25 canoperate on point SNR values (equal to I_(n)(x) in this example), oralternately the estimator 25 can operate with an average SNR over aportion of the image signal. The region over which the estimator 25operates can be a portion of a current image frame, an entire currentimage frame, a previous image frame, or two or more image frames. Theestimator 25 generates an output signal I_(m)(x) which defines the localmaximum SNR in the selected portion of the input signal.

The adaptive multi-dimensional mapping stage 26 responds to the inputsignals I_(n)(x), {overscore (I)}_(t)(x), and I_(m)(x) to generate anoutput signal D(x) for display. In the embodiment of FIG. 2, the stage26 is shown as a single stage that can be implemented as a singlelook-up table. Alternatively, the various functions performed by themapping stage 26 can be implemented by a variety of mapping devicesincluding adders, multipliers and look-up tables, which can be placedtogether or separately at various stages in the signal path between thereceive beamformer 14 and the display 19.

In the example of FIG. 2, the mapping stage 26 implements the followingequation:

 D(x)=g(I(x), {overscore (N)}(x), {overscore (I)} _(t)(x), I _(m)(x),D_(t) D _(m), . . . )  (Eq. 4)

The function g implemented by the mapping stage 26 can be the functionidentified in Eq. 5 and illustrated in FIG. 4. $\begin{matrix}{{D(x)} = \left\{ \quad \begin{matrix}{D_{n} = 0} & {{I_{n}(x)} \leq 0} \\{\frac{D_{t}}{{\overset{\_}{I}}_{t}(x)}\quad {I_{n}(x)}} & {0 < {I_{n}(x)} \leq {{\overset{\_}{I}}_{t}(x)}} \\{\frac{D_{m} - D_{t}}{{I_{m}(x)} - {{\overset{\_}{I}}_{t}(x)}}\left( {{I_{n}(x)} - {{\overset{\_}{I}}_{t}(x)}} \right)} & {{{\overset{\_}{I}}_{t}(x)} < {I_{n}(x)} \geq {I_{m}(x)}} \\D_{m} & {{I_{n}(x)} > {I_{m}(x)}}\end{matrix}\quad \right.} & \left( {{Eq}.\quad 5} \right)\end{matrix}$

where I_(n)(x)=I(x)−{overscore (N)}(x).

Note that values of I_(n)(x) equal to 0 are mapped to the noise targetdisplay value D_(n), which equals 0 in this example. Values of thesignal I_(n)(x) equal to the local tissue mean {overscore (I)}_(t)(x)are mapped to the soft tissue target display value D_(t), and values ofthe signal I_(n)(x) equal to the local maximum SNR I_(m)(x) are setequal to the maximum target display value D_(m). D_(t) may be set to anydesired value, and may be user-selectable. In one example, D_(t)=64 on alinear scale where 0 and 255 map to the darkest and brightest displayvalues, respectively. In another example D_(t)=84 on such a scale.

The mapping function g of Equation 4 can of course be varied from theforegoing example. For example, as shown in FIG. 5 a polynomial splinefunction may be used to eliminate the discontinuity in the slope of themap around I_(n)(x)={overscore (I)}_(t)(x) This results in a lessprecise mapping of I_(n)(x) to D_(t) when I_(n)(x) is equal to the localsoft tissue mean.

FIG. 6 shows another approach that controls the slope of the curvearound values of I_(n)(x)={overscore (I)}_(t)(x).

Other alternatives are possible. For example, as shown in FIG. 3 anSNR-adaptive spatial and temporal persistence filter can be interposedbetween the summer 24 and the mapping stage 26. This filter can be usedto reduce noise for input values with low SNR, without giving uptemporal or spatial resolution for input signals with sufficient SNR.

FIG. 7 provides a flow chart of a method implemented by the embodimentof FIG. 2. In block 27 a, a local-mean noise signal {overscore (N)}(x)is provided that is indicative of the currently prevailing system noiselevel. This can be done using noise frames as described above.Alternatively, a computer model of the imaging system can be used toestimate the local noise mean as a function of the currently prevailingimaging parameters.

Other approaches also can be used. The local noise mean {overscore(N)}(x) may vary as a function of one, two or more spatial dimensions ofthe image. Thus, the term “providing” is intended broadly and is notlimited to any single approach.

In 27 b those portions of I(x) acquired from soft tissue are identifiedbased on a statistical measure of variability of I(x) and on {overscore(N)}(x), and these portions are then used to determine a local softtissue mean {overscore (I)}_(t)(x). As explained above, {overscore(I)}_(t)(x) is representative of the mean or average amplitude of thenoise-suppressed input signal for those portions of the input signalrepresentative of soft tissue.

At 27 c the maximum SNR I_(n)(x) of I(x) is estimated over a specifiedregion, which as explained above may vary depending upon theapplication.

At 27 d an input signal I(x) is mapped to D_(n) when I(x) is comparableto {overscore (N)}(x). That is, the output signal D(x) is set equal to aselected value when I(x) is comparable to {overscore (N)}(x). Since{overscore (N)}(x) varies locally across an image, this provides theadvantage that throughout the image values of the input signal I(x)comparable to the noise level are mapped to a range of output signallevels around D_(n).

At 27 e the noise suppressed input signal is mapped to D_(t) when it isequal to {overscore (I)}_(t)(x). In this way the input signal I(x) thatis acquired from soft tissue is mapped to a soft tissue range of valuesof D(x) around D_(t). Note that block 27 e is specific to B-mode typeinput signals.

At 27 f I(x) is mapped to a high SNR range of D(x) values around D_(m)whenever the SNR of I(x) is comparable to I_(m)(x). In addition toB-mode type input signals, block 27 f is also applicable to ColorDoppler Power Mode and Spectral Doppler Mode input signals.

From this explanation it should be apparent that the system of FIG. 2adaptively maps the input signal I(x) in such a way as to present theuser with an image that meets the following three criteria:

1. Throughout the image frame, input signals that are comparable tonoise are mapped to a range of values around a noise target value D_(n).

2. Throughout the image frame, B-mode type input signals associated withsoft tissue are mapped to a range of values around a soft tissue targetvalue D_(t).

3. Throughout the image frame, input signals having an SNR comparable tothe local maximum SNR are mapped to a range of values around a targetdisplay value D_(m).

For example, D_(n) may be at or near black, D_(m) may be at or nearwhite, and D, may be a specified range of gray levels, e.g., gray levelsaround 64 in a system where 0 corresponds to black and 255 to white.

The parameter x is used to signify a point on any one, any two, anythree, or all four of the azimuth, range, elevation and time (framenumber) axes or dimensions.

Of course, many variations are possible. For example, it is not requiredthat the darkest possible display level be associated with the localmean noise level and that the brightest possible display level beassociated with the local maximum SNR. If desired, a narrower range ofdisplay levels can be used. For example, the input signal can be mappedto a range of display levels smaller than the total number of availablelevels. Also, the mapping functions described above may be used incombination with other constraints. For example, constraints may beapplied to how fast {overscore (I)}_(t)(x) and or I_(m)(x) can varyacross the image or from frame to frame. Also, D_(t) can be varied asfunction of SNR to address the cases where the local mean soft tissuelevel is too close to the local mean noise level. It is also notessential that the input signal itself be mapped to the various targetlevels described above. In an alternative embodiment a function of theinput signal may be mapped to these levels.

Additional Embodiments

It is not essential in all embodiments that all of the functionsdescribed above in conjunction with FIG. 2 be combined. Variousgroupings of selected ones of these functions are also useful. Forexample, the embodiment described below in conjunction with FIG. 9controls the local gain in both the near field and far field of theimage such that soft tissue is displayed at a substantially constanttarget value. The embodiments described below in conjunction with FIGS.14 and 15 additionally adjust the dynamic range of the displayed imagelocally to optimize the display in view of the currently-prevailingimage signals.

FIG. 8 provides a block diagram of these embodiments of a mapping stage18′ of FIG. 1. As shown in FIG. 8, the mapping stage 18′ includes anoise frame processor 30, a soft tissue processor 32, and a gainprocessor 34. The noise frame processor 30 generates an estimate ofelectronic noise as it varies over the frame. The soft tissue processor32 generates a smoothed surface indicative of the intensity of softtissue within an image frame at various locations in the frame. The gainprocessor 34 uses outputs from the processors 30 and 32 to adaptivelyadjust either the gain or both the gain and the dynamic range applied tothe image frame.

FIG. 9 provides a more detailed block diagram of the one preferredembodiment of the elements of FIG. 8, and FIG. 10 provides a flow chartof a method implemented by the embodiment of FIG. 9.

As shown in FIG. 9, the noise frame processor 30 in this embodimentincludes a low pass filter 40 and a decimator 42, and the processor 30generates a measure of average electronic noise at various locationsdistributed throughout the frame. The noise processor 30 accepts as aninput a noise frame, i.e. a frame of image data acquired with thetransmitters turned off. The low pass filter 40 smoothes the noiseframe, and the decimator 42 decimates the filtered noise to a coarsergrid, measuring for example 50 pixels on a side. Other decimationfactors can be used, such as a decimation factor of 10×10 pixels on theacoustic grid.

The soft tissue processor 32 responds to an image frame of data which isacquired with standardized-imaging parameters as described below andwhich includes data from soft tissue in the image. The soft tissueprocessor 32 includes a low pass filter 44 and a decimator 46 that arepreferably identical to the corresponding elements of the noiseprocessor 30. The filtered, decimated noise frame from the noiseprocessor 30 is summed with negative polarity with the filtered,decimated image frame in a summer 54. Since the noise frame and theimage frame are in this example post-detection, post-compressionsignals, the summation performed by the summer 54 generates an outputsignal equal to the signal to noise ratio (SNR) for the associatedregion of the two frames. This SNR signal is applied to a comparator 56that generates as an output an SNR binary image. This binary image isset equal to one in regions of the frame characterized by an SNR greaterthan a predetermined value, e.g., 3 dB or 6 dB, and to zero in regionswhere the SNR is less than or equal to the predetermined value. Thus theSNR binary image identifies regions of the image frame that have asufficiently high SNR to be candidates for soft tissue image signals.The portions of the SNR binary image characterized by the logic valuezero correspond to high-noise, low-SNR regions of the image, and theseregions are not considered candidates for soft tissue.

The soft tissue processor 32 also generates a variance binary image bymeans of a local variance calculator 48, a decimator 50 and a comparator52. These elements use the local spatial variance of the image frame toidentify regions of the image frame having a variance characteristic ofsoft tissue.

In soft tissue there are a large number of scatterers present in eachresolution cell. Fully developed speckle occurs due to randominterference between the reflected signals, and the amplitude of thesignal obeys the Rayleigh distribution in regions of the image framedepicting soft tissue. In this embodiment, the degree to which localvariance, calculated in a few resolution cells around each image pixel,resembles that of fully developed speckle is used as a measure of thelikelihood that a particular image pixel represents an image of a softtissue. The variance binary image is set equal to one in regions wherethe variance is consistent with soft tissue imaging and to zerootherwise.

The local variance calculator 48 operates by dividing the image into agrid of smaller regions. The size of these regions is preferably on theorder of 10 times longer along each axis than the resolution size of theimage.

The spatial variance V_(i,j) of the center of a region or cell C havingthe coordinates (i,j) can be calculated as follows: $\begin{matrix}{V_{i,j} = {\frac{1}{N^{2}}{\sum\limits_{k,{l = 1}}^{N}\quad {\left( {I_{{i + k},{j + 1}} - {\langle I\rangle}} \right)^{2}.}}}} & \left( {{Eq}.\quad 6} \right)\end{matrix}$

The decimator 50 preferably operates at the same scale as the decimators42 and 46. The decimated variance frame is then compared element byelement with minimum and maximum variance levels in the comparator 52.This comparison is particularly straightforward for log compressed data,where the variance of fully developed speckle characteristic of softtissue is (5.57 dB)². Thus, regions of soft tissue in the image framewill be characterized by fully developed speckle having a variance closeto (5.57 dB)². For example, the comparator 52 of FIG. 9 can classify avariance as characteristic of soft tissue if it meets the relationshipset out in Eq. 7: $\begin{matrix}{\frac{{{Var} - (5.57)^{2}}}{(5.57)^{2}} < {0.5.}} & \left( {{Eq}.\quad 7} \right)\end{matrix}$

The actual local variance of speckle may not be equal to the theoreticalvalue due to filters in the signal processing path of the ultrasoundsystem. In practice the variance is determined through measurements onphantoms mimicking soft tissue.

Electronic noise itself has a variance close to that of soft tissue, andthe AND operation indicated at 60 uses the SNR binary image and thevariance binary image to avoid misclassification of electronic noise assoft tissue. This AND operation is performed on an element-by-elementbasis of the decimated SNR binary image and the decimated variancebinary image. The resulting decimated tissue binary image has a valueequal to zero if either the SNR binary image indicates that theassociated region is characterized by low SNR ratio or the variancebinary image indicates that the associated region is not soft tissue.The SNR binary image is not required in all embodiments, and othertechniques can be used to avoid misclassifying regions of the imagedominated by noise as soft tissue. For example, noise reductiontechniques can be applied prior to local variance estimation.

The filtered, decimated image frame from the decimator 46 and the binarytissue image from the AND element 60 are applied as inputs to a device62 for computing soft tissue intensity. In particular, the output of thedevice 62 is a decimated frame having intensity values that depend uponthe corresponding values of the tissue binary image in the same region.Where the corresponding region of the tissue binary image is equal tologic value zero (indicating that the region does not correspond to softtissue), the output of the device 62 does not include an intensity valuefor the corresponding region. Alternatively, for regions where thetissue binary image is equal to logic value one, the output of thedevice 62 includes the intensity value for the corresponding region asfiltered by the filter 44 and decimated by the decimator 46.

In device 64 a surface, e.g., a second order surface, is fitted to theframe supplied by the device 62. This second order surface provides ameasure of average soft tissue intensity as it varies throughout theimage frame. Because of the use of the SNR binary image, portions of theimage dominated by noise do not corrupt this second order surface.Because the surface is a second order surface fitted to a decimatedframe, the surface fitted by device 64 does not vary so rapidly as tointerfere with the presentation of transitions or interfaces betweensoft tissues of different contrasts. In one embodiment, the device 64divides the image into a 6×6 grid, calculates the average soft tissueintensity value for each rectangular region of the grid, and then fits asecond order surface to the average values.

Continuing with FIG. 9, the gain processor 34 of this embodiment uses asummer 82 to obtain the difference between the fitted surface from thedevice 64 and a soft tissue target intensity level T_(T) on aregion-by-region basis. The output of the summer 82 is a tissue gainG_(T), which varies with both range and azimuth and is the gain requiredto cause the surface fitted to the local tissue mean to be displayed atthe soft tissue target level T_(T). This tissue gain G_(T) is applied toa logic block 84 that also receives a second input G_(N). The signalG_(N) is generated by a summer 80 that takes the difference on apoint-by-point basis between a noise target level T_(N) andcorresponding values of the filtered, decimated noise frame. Thus, thenoise gain G_(N) also varies with both range and azimuth, and representsthe gain that is required to ensure that the local mean noise level ispresented at the noise target level T_(N). The logic block 84 sets thefinal two-dimensional gain equal G_(F) to the lesser of G_(N) and G_(T).This final two-dimensional gain G_(F) is applied to the image frame inblock 86. In some embodiments, the final gain G_(F) is decomposed intodepth gain, lateral gain and lateral gain slope components, e.g. via aleast squares fit. It may be preferable to chose depth gain componentsto minimize lateral gain slope values, and the master gain value tominimize changes in depth gain and lateral gain.

FIGS. 11-13 illustrate operation of the gain processor 34 of FIG. 9. InFIG. 1 the soft tissue target level T_(T) and the noise target levelT_(N) are shown in dotted lines. In this case, T_(T) and T_(N) are bothconstant with depth. The noise intensity I_(N) supplied by the decimator42 and the tissue intensity I_(T) supplied by the device 64 are shown insolid lines. In FIG. 12 G_(T) and G_(N) are illustrated, and in FIG. 13the final gain G_(F) is shown as the lesser of G_(T) and G_(N).

FIGS. 11-13 have been presented as two-dimensional graphs of intensityas a function of depth for clarity of illustration. As explained above,the gains G_(T), G_(N) and G_(F) all vary in two dimensions as afunction of both depth and azimuth.

The gain processor 34 sets the gain G_(F) in such a way that the softtissue regions of the image are displayed at about the tissue targetlevel T_(T) for all portions of the image where the noise signal is lessthan the noise target level. In regions of the image where the noiseintensity I_(N) is greater than a noise target level T_(N) a lower gainis used to ensure that noise is not amplified inappropriately.

FIG. 10 provides a flowchart of a method implemented by the system ofFIG. 9. In block 100 the adaptive gain features described above areinitiated. This can be done in many ways. For example, adaptive gain canbe initiated in response to user request or automatically at intervals.For example, adaptive gain can be automatically initiated every setnumber of frames or seconds.

Once adaptive gain has been initiated in block 100, control passes toblock 102 where the image acquisition parameters of the system are setto preselected values. These preselected values optimize operation ofthe adaptive gain processor. By way of example the following generalguidelines have been found suitable in one embodiment:

Image acquisition parameters, including gain and dynamic range, aredetermined so that, for the widest possible variety of imagingsituations, the highest possible signal to noise ratio is maintainedover the entire image without causing saturation of any portion of theimage. This ensures that areas where the signal is weak are taken intoaccount by the adaptive gain processor.

Once the image acquisition parameters have been selected, they are usedto acquire one or more noise frames in block 104 and an image frame inblock 106. As explained above, a noise frame is a conventional imageframe, except that the transmitters are turned off. Since thetransmitters are turned off there is no bona fide echo signal, and anysignal appearing in the image frame is representative of system orelectronic noise. The noise frame is used in block 108 to identifyregions of image characterized by low SNR, as discussed above inconjunction with the creation of the SNR binary image. The image framecan be in any desired modality, and can for example include fundamentalor harmonic imaging of tissue with or without added contrast agent.

Next, in block 110, a statistical measure of variability is determinedfor selected regions of the image frame. In block 110 the spatial ortemporal mean of amplitude-detected, log-compressed signals can be usedas described above. Alternately, the spatial variance of noise powernormalized by the local mean of noise power can be used. For example, anormalized spatial variance can be determined on a pre-compressionsignal, where the normalized spatial variance is normalized by the localmean of the precompression signal.

The statistical measure of variability may be calculated along any oneof the lateral, axial, and elevational axis, any two of these axes, orall three axes. The example described above calculates the variance overthe lateral and axial axes.

Next, in blocks 112 and 114, regions of the image frame corresponding tosoft tissue are determined. In block 114, the regions of the imagecharacterized by low SNR as determined in block 108 are used to ensurethat regions identified as soft tissue are outside of thenoise-dominated regions of the image.

The local coherence factor may be used to ensure that regions of highacoustic noise or clutter are excluded from mapping decisions. The localcoherence factor is defined as the ratio of the coherent(phase-sensitive) to the incoherent (phase-insensitive) summation acrossthe receive channels of the delayed and apodized signals. See thediscussion of Rigby U.S. Pat. No. 5,910,115. A low coherence factorindicates strong phase aberration, i.e., high levels of acoustic noiseor clutter. Therefore using the coherence factor the regions of theimage dominated by clutter can be ignored.

As explained above, this soft tissue identification can be done based onstatistical measures of variability. Alternately, in some embodimentsother methods may be used for identifying soft tissue, as for examplemethods based on the magnitude of the image signal. See the discussionof Klesenski U.S. Pat. No. 5,579,768, assigned to the assignee of thepresent invention.

At 116 a second order surface is fitted to the soft tissue intensityvalues over an entire frame, including both near-field and far-fieldportions of the frame.

At 118 the local gain is varied adaptively to cause signals having theamplitude of the second order surface at the respective locations to bedisplayed at a soft tissue target value over some or all of the image.The soft tissue target value or target display value can be set in manyways. The target display value may simply be a stored value or may be auser-selected value, or it may be a value adaptively determined inresponse to ambient light.

Alternatively and preferably, the soft tissue target level is a functionof the currently invoked post-processing curve. Specifically a usercontrollable or predefined value may be used as a target soft-tissuegray level T_(G). T_(T) is then defined whenever a post-processing curveis selected to be the signal intensity level that is mapped to thedisplay gray level of T_(G).

FIG. 14 represents a second preferred embodiment of the gain processorof FIG. 8. The embodiment of FIG. 14 includes the summers 80, 82, thelogic block 84 and the block 86, which may be identical to thecorresponding elements of FIG. 9 discussed above. Additionally, the gainprocessor of FIG. 14 adaptively sets the dynamic range with which theimage frame is displayed. In block 140 the final gain G_(F), the tissuegain G_(T) and a previously selected dynamic range DNR_(OLD) are used togenerate a new dynamic range DNR_(NEW) according to the followingequation: $\begin{matrix}{{DNR}_{NEW} = {\left( \frac{T_{T} - G_{F}}{T_{T} - G_{T}} \right)\quad {{DNR}_{OLD}.}}} & \left( {{Eq}.\quad 8} \right)\end{matrix}$

This new dynamic range DNR_(NEW) is then applied to the gain-adjustedimage generated by the block 86 to form a DNR-adjusted image to beapplied to the display.

The gain processor of FIG. 14 adjusts the dynamic range in the low SNRregion of the image. This adjustment of the dynamic range ensures thatthe soft tissue is on average displayed at the preselected target valueT_(T). Since the final gain G_(F) and the tissue gain G_(T) arefunctions of depth and azimuth, the new dynamic range DNR_(NEW) is aspatially varying, adaptively determined quantity even when DNR_(OLD) isnot. The dynamic gain processor of FIG. 14 adaptively adjusts the SNRonly in low SNR regions of the image, because in high SNR regions thefinal gain G is equal to the tissue gain G_(T) and therefore in high SNRregions DNR_(NEW) is equal to DNR_(OLD).

The blocks 140, 142 of FIG. 14 adaptively vary the dynamic range of asignal based on both the soft tissue intensity and the noise level at aplurality of locations within the image.

FIG. 15 shows a third preferred embodiment of the gain processor of FIG.8. In the gain processor of FIG. 15, block 160 sets the parameter Saccording to the following equation: $\begin{matrix}{{S = \left( \frac{T_{T} - T_{N}}{I_{T} - I_{N}} \right)},} & \left( {{Eq}.\quad 9} \right)\end{matrix}$

where I_(T) is the local mean tissue intensity, I_(N) is the local meannoise level, T_(T) is the tissue target intensity and T_(N) is the noisetarget intensity. In block 162 the dynamic range DNR is set equal to theminimum of the value S as determined by the block 160 and a maximumallowable dynamic range DNR_(MAX). In block 164 a gain parameter is setaccording to Eq. 10: $\begin{matrix}{G_{F} = {\frac{T_{T}}{DNR} - {I_{T}.}}} & \left( {{Eq}.\quad 10} \right)\end{matrix}$

Then, in block 166 the gain determined in block 164 and the dynamicrange determined in block 162 are applied to the image frame.

FIG. 16 will be used to explain the operation of the gain processor ofFIG. 15. As shown in FIG. 16 the input signal is plotted on thehorizontal axis in units of dB and the display gray level is plotted onthe vertical axis. Here the slope of the line mapping the input signalto the display gray levels is inversely proportional to the dynamicrange, in units of gray level per dB. Of course, the mapping functionbetween the signal and the gray levels does not have to be linear, andin this case the slope of the line joining the gray levels thatcorrespond to the minimum and maximum of the input signal of interestcan be used. The gain in this example appears as shown, and the mappedgray level is equal to the slope multiplied by the sum of the signalplus the gain. With this relationship, the gain and dynamic range asdetermined by the embodiment of FIG. 15 map the average soft tissueintensity and the noise to the respective desired target values.

Further Discussion

1. Soft Tissue Detection

The preferred embodiments described above use statistical measures ofvariability of an input signal to identify soft tissue. The followingsteps can be taken (individually or in various combinations) to improvethe accuracy of soft tissue identification and to reduce false positiveidentifications of soft tissue.

a. Identity regions of low SNR and do not classify such regions as softtissue—The elements 54, 56, 60 of FIG. 9 implement this function, asdescribed above.

b. Provide adjustable thresholds for comparison with the local measureof variability—The element 52 of FIG. 9 can use maximum and minimumthresholds that are adjusted, either by the user or automatically as afunction of the particular transducer or image processing parametersthat are in use. The maximum and minimum thresholds may be positionedasymmetrically or symmetrically with respect to a target value. In someapplications, it may be preferable to select values for the thresholdsthat substantially eliminate false positive identification of softtissue, even at the expense of somewhat increased false negativeidentification of soft tissue.

c. Turn off signal processing that affects the statistical measure ofvariability—Conventional signal processing techniques such as frequencycompounding, spatial compounding, spatial filtering (e.g., with videofilters), temporal filtering (e.g., persistence filtering), andnon-linear post-processing mapping often affect statistical measures ofvariability. For this reason, such signal processing is preferablyturned off during acquisition of the input signal used for soft tissueidentification, or alternately the effects of such signal processing aretaken into account in soft tissue identification, e.g., by settingthreshold values appropriately. Additionally, spatial under-sampling canresult in image artifacts that alter local variance, and such artifactsare therefore preferably avoided.

d. Reduce variance (i.e., noise) in the estimate of the statisticalmeasure of variability—The measure of variability for each pixel can beestimated using a larger region of support than that described above, orthe variability estimates can be spatially low-pass filtered, therebycompromising detail resolution of the variability estimate for a lowerestimation noise. In one alternative, the area used to calculate localvariance (or other statistical measure of variability) is a function ofaverage speckle size, e.g., specified in units of average speckle size.The average speckle size is given by the equivalent width of theautocovariance function, and it is directly determined by the size ofthe round-trip point spread function, or equivalently by the lateral andaxial bandwidths of the beamformer. This parameter can be set separatelyfor each transducer in units of acoustic grid (pre-scan conversion)samples, since the acoustic grid more or less tracks changes in lateraland axial bandwidth.

In the dynamic update case, time averaging (persisting) the varianceimage, or the resulting tissue mask or the adaptive gain image may beneeded to prevent flickering or sudden gain changes, due to noise in thevariance estimate.

e. Use other measures, in addition to the statistical estimate of themeasure of variability, to improve soft tissue identification—Forexample, tissues such as tendon and muscle are characterized by reducedspeckle along the long axis of the tendon or muscle. This pattern may beidentified and used to improve the accuracy with which these types oftissue can be identified. As another example, contrast agent in softtissue tends to be depleted by ultrasound beams, and this depletioncauses a lack of correlation between successive images. This lack ofcorrelation can be detected and then used in combination with the softtissue identification methods described above to improve the accuracywith which contrast agent in soft tissue is identified.

2. Statistical Measure of Variability

The foregoing discussion has used variance as one example of astatistical measure of variability. The standard definition of variance(σ²) is the expected value of the magnitude square of the differencebetween the statistical variable and its expected value.

σ² =<|I−<I>| ²>,

or equivalently

σ² =<|I| ² >−|<I>| ²,

where <.> is the expected value, i.e., the mean operator, and |.| is themagnitude operator. The element 48 of FIG. 9 uses the local spatialvariance. For any pixel x0 in the image, the local variance is computedusing the samples in a region R around the pixel x0, where region R canbe defined on any one or multiple axes. The axes can be the axial,lateral, elevational or any other arbitrary spatial axis.

There are numerous approximations to the spatial variance, and all canbe taken as examples of other statistical measures of variability thatcan be used here. For example, assuming that the mean is negligible orthat it is more or less constant across the image, the variance can beapproximated by

σ² <˜|I| ²>.

Other possible expressions include the following:

σ²˜(max(I)−min(I) )/<I>;

σ²˜(max(I)−<I>)/<I>;

σ²˜(<I>−min(I) )/<I>;

σ²˜(max(I)−min(I) );

σ²˜(max(I)<I>);

σ²˜(<I>−min(I) ).

Here again the max(.) and min(.) operations, as well as the meanoperator, are performed over a region R around the pixel x0.

As another example, it may be preferred to measure the spectrum ofspatial frequencies of the input sample along one or more axes, and thento compare this spectrum with the spectral characteristic of softtissue.

Other statistical measures of variability include parameters that varyas a function of variance, e.g., standard deviation (y, or anapproximation of variance.

The term “statistical measure of variability” is intended broadly toinclude all of these examples, as well as other statistical measuresthat can be used to identify soft tissue or to differentiate other typesof tissue from soft tissue.

3. Surface Fitting

The surface fitting function performed in block 64 of FIG. 9 can beimplemented in many ways. For example, polynomial splines, which arepiecewise polynomial surfaces with derivative continuities up to a ispredetermined order at the borders between the pieces of polynomialsurfaces, can be used. The order of the polynomial splines determinesglobal smoothness, while the number of derivative continuities satisfiedat the borders between the pieces of polynomial surfaces determines thedegree of local smoothness.

Any function which can be defined as a sum of (orthogonal ornonorthogonal) basis functions can also be used for surface fitting,such as functions that can be written as a sum of trigonometric orhyperbolic trigonometric functions. In general, linear combinations ofarbitrary basis functions of range and azimuth can be used, withweighting parameters chosen to fit the grid of average soft-tissueintensity values.

4. Initiation of Adaptive Adjustment of Gain or Dynamic Range

As explained above, the adaptive adjustments described above can beinitiated manually or automatically at intervals. In addition, suchadjustments may be initiated automatically in response to a large changein the input signal, e.g., a large change in the frame sum (the sum ofall B-mode pixels in a frame) or in a sum of a region of a frame, or alarge motion detected based on frame correlation. For example, twosequential frames may first be spatially filtered by, say, a box-carfilter and decimated, and then the squares of the differences betweenthe values of the decimated pixels of the two frames are summed. If thissum exceeds a predetermined threshold, e.g., 20% of the total energy(sum of magnitude square) of the first of the two decimated frames, themethod of FIG. 7 or 10 is initiated. Variations include using a functionother than squaring, limiting the calculations to a portion of a frame(e.g., a central portion), monitoring a Doppler signal to gauge when auser has stopped moving the probe (possibly with an added delay toensure that the user has achieved a useful probe position). Anotherapproach is to initiate the method of FIG. 7 or 10 in response to userchange of an imaging parameter (e.g. transmit or receive ultrasoundfrequency).

5. Use with Resolution-enhancing Display Modes

In conventional resolution-enhancing modes of operation, a portion of anexisting frame is expanded and displayed. This can be done byreacquiring the portion of the frame at higher resolution or byincreasing a magnification factor applied to the existing frame. Ineither case, input signals for a region that is larger than the expandedportion can be used in the tissue-identifying and surface-fittingmethods described above. This may reduce edge ambiguities and artifactsin the expanded portion, and it may allow a more robust gain surface tobe calculated in cases where there is little soft tissue in the expandedportion.

Conclusion

Of course, many alternatives are possible. In fact, the widest range ofanalog and digital signal processing techniques can be used to implementthe basic functions described above. A programmed computer is onepreferred implementation for the adaptive gain processor describedabove. For example, the adaptive gain and optionally the adaptivelydetermined dynamic range can be applied to the image signals at anydesired point along the signal path between the transducer array 13 andthe display 19. Both can be applied before or after scan conversion, logcompression and detection. The adaptive gain processor can operate onRF, IF or baseband signals before or after detection, log compression,and scan conversion.

Also, other methods can be used to determine the noise levels. Forexample, a computer model can be used that calculates noise level forvarious positions in the frame base on the parameters (including theacquisition parameters) of the imaging system.

In the foregoing examples, the input signal is adaptively mapped to asoft tissue or a noise range of output signal values by appropriatelycontrolling various back-end gain stages. However, the invention is notlimited to this approach, and front-end gain stages may be varied ingain to obtain the desired output signal values, either alone, or incombination with gain variations in one or more back-end gain stages.

The preferred embodiments described above combine a number of featuresthat work together efficiently to adaptively set the gain and dynamicrange of an image. It will be recognized that various ones of thesefeatures can be used separately from one another rather than incombination. In particular, the following inventions can be usedtogether or in various subcombinations:

Using a statistical measure of variability to identify areas of an imagecorresponding substantially to soft tissue;

Fitting a surface to soft tissue intensity values, including soft tissueintensity values in both the near-field and the far-field portions of animage;

Acquiring an image for an adaptive gain system with a plurality ofacquisition parameters of the system set to respective preselectedvalues;

Adaptively varying a gain of an ultrasonic imaging system based at leastin part on soft tissue intensity values and noise values atcorresponding locations;

Adaptively varying dynamic range of an ultrasonic imaging system basedat least in part on soft tissue intensity values and noise values at aplurality of locations in the image.

As used herein the term “image” is intended broadly to encompass imagesof one, two or three spatial dimensions. For example, an M-mode displaycan be considered a one-dimensional image.

The term “range of values” is intended broadly to encompass one or morevalues.

Two signals are said to be “comparable” whether they are equal in scalefactor or differing in scale factor.

The term “soft tissue” is intended to refer to any target that producesspeckle because of its unresolvable fine structure.

As indicated above, the maximum value of SNR may vary with time and anyset of spatial coordinates.

The foregoing detailed description has been intended by way ofillustration and not limitation. It is only the following claims,including all equivalents, that are intended to define the scope of thisinvention.

What is claimed is:
 1. In a medical ultrasonic imaging system operativeto acquire a receive input signal and to display an output signal, anadaptive mapping method comprising: (a) providing a noise signalindicative of a currently-prevailing noise level for the system; (b)mapping the input signal to a noise range of output signal values whenthe input signal is comparable to the noise signal; and (c) mapping theinput signal to a soft tissue range of output signal values when theinput signal is acquired from soft tissue.
 2. The method of claim 1wherein (c) comprises mapping the input signal to a high-SNR range ofoutput signal values when an SNR of the input signal relative to thenoise signal is comparable to a maximum SNR of the input signal.
 3. In amedical ultrasonic imaging system operative to acquire a receive inputsignal and to display an output signal, an adaptive mapping methodcomprising: (a) providing a noise signal indicative of acurrently-prevailing noise level for the system; (b) mapping the inputsignal to a noise range of output signal values when the input signal iscomparable to the noise signal; and (c) mapping the input signal to ahigh-SNR range of output signal values when an SNR of the input signalrelative to the noise signal is comparable to a maximum SNR of the inputsignal.
 4. In a medical ultrasonic imaging system operative to acquire areceive input signal and to display an output signal, an adaptivemapping method comprising: (a) providing a noise signal indicative of acurrently-prevailing noise level for the system; and (b) mapping theinput signal to a high-SNR range of output signal values when an SNR ofthe input signal relative to the noise signal is comparable to a maximumSNR of the input signal.
 5. The method of claim 4 further comprising:(c) mapping the input signal to a noise range of output signal valueswhen the input signal is comparable to the noise signal.
 6. The methodof claim 4 further comprising: (c) mapping the input signal to a softtissue range of output signal values when the input signal is acquiredfrom soft tissue.
 7. In a medical ultrasonic imaging system operative toacquire a receive input signal and to display an output signal, anadaptive mapping method comprising: (a) determining a statisticalmeasure of variability of the input signal; (b) identifying portions ofthe input signal corresponding to soft tissue based at least in part onthe statistical measure of (a); and (c) mapping the portions of theinput signal identified in (b) to a soft tissue range of output signalvalues.
 8. The method of claim 7 further comprising: (d) providing anoise signal indicative of a currently-prevailing noise level for thesystem; (e) mapping the input signal to a noise range of output signalvalues when the input signal is comparable to the noise signal.
 9. Themethod of claim 8 further comprising: (f) mapping the input signal to ahigh-SNR range of output signal values when an SNR of the input signalrelatives to the noise signal is comparable to a maximum SNR of theinput signal.
 10. The method of claim 7 further comprising: (d) mappingthe input signal to a high-SNR range of output signal values when an SNRof the input signal relative to the noise signal is comparable to amaximum SNR of the input signal.
 11. The method of claim 8 wherein (b)comprises: (b1) determining high clutter portions of the output signalcharacterized by low coherence factor; and (b2) ensuring that the areasidentified in (b) are outside of the high clutter regions determined in(b1).
 12. The method of claim 1, 3, 4, or 7 wherein the input signal isindicative of a multidimensional image.
 13. The method of claim 2, 4, 10or 9 wherein the SNR of the input signal is indicative of a point SNR.14. The method of claim 13 wherein the maximum SNR is determined over aportion of the input signal corresponding to a portion of a currentimage frame.
 15. The method of claim 14 wherein the maximum SNR isdetermined over a portion of the input signal corresponding to an entirecurrent image frame.
 16. The method of claim 14 wherein the maximum SNRis determined over a portion of the input signal corresponding to aprevious image frame.
 17. The method of claim 14 wherein the maximum SNRis determined over a portion of the input signal corresponding to Nprevious image frames, where N is an integer greater than
 1. 18. Themethod of claim 3, 4, 10 or 9 wherein the SNR of the input signal isindicative of an average SNR.
 19. The method of claim 18 wherein themaximum SNR is determined over a portion of the input signalcorresponding to a portion of a current image frame.
 20. The method ofclaim 18 wherein the maximum SNR is determined over a portion of theinput signal corresponding to an entire current image frame.
 21. Themethod of claim 18 wherein the maximum SNR is determined over a portionof the input signal corresponding to a previous image frame.
 22. Themethod of claim 18 wherein the maximum SNR is determined over a portionof the input signal corresponding to N previous image frames, where N isan integer greater than
 1. 23. The method of claim 1, 3, 4, 10 or 9wherein the noise signal varies as a function of at least two spatialdimensions.
 24. The method of claim 2, 3, 4, 10 or 9 wherein the maximumSNR of the input signal varies as a function of at least one spatialdimension.
 25. The method of claim 2, 3, 4, 10 or 9 wherein the maximumSNR of the input signal varies as a function of at least two spatialdimensions.
 26. The method of claim 1, 4 or 7 wherein the receive inputsignal is a signal selected from the group consisting of an intensitysignal and an amplitude signal.
 27. The method of claim 1, 4 or 7wherein the receive input signal is a B-mode signal.
 28. The method ofclaim 1, 4 or 7 wherein the receive input signal is a log-compressedsignal.
 29. In a medical ultrasonic imaging system operative to acquirean input signal indicative of an echo signal parameter and to display anoutput signal, an adaptive mapping method comprising: (a) determining astatistical measure of amplitude variability of the input signal; (b)identifying portions of the input signal corresponding substantially tosoft tissue based at least in part on the statistical measure of (a);(c) causing average amplitude of the portions of the input signalidentified in (b) to be displayed at substantially a target displayvalue.
 30. The method of claim 29 wherein the statistical measure of (a)is indicative of spatial variance of the input signal, and wherein theinput signal is amplitude-detected and log-compressed signals.
 31. Themethod of claim 29 wherein the statistical measure of (a) is indicativeof spatial variance of the input signal normalized by spatial local meanof the input signal, and wherein the input signal is a pre-compressionsignal.
 32. The method of claim 29 wherein the statistical measure isdetermined in (a) along at least one axis selected from the groupconsisting of lateral, axial and elevational axes.
 33. The method ofclaim 29 further comprising: (d) storing the target display value as apre-selected value.
 34. The method of claim 29 further comprising: (d)accepting user selection of the target display value.
 35. The method ofclaim 29 further comprising: (d) adaptively adjusting the target displayvalue in response to ambient light.
 36. The method of claim 29 whereinact (a) is automatically performed at a predetermined interval, andwherein the predetermined interval comprises a predetermined number ofimage frames.
 37. The method of claim 29 wherein act (a) isautomatically performed at a predetermined interval, and wherein thepredetermined interval comprises a predetermined number of seconds. 38.The method of claim 29 wherein (b) comprises: (b1) determining noiseregions of the image characterized by low SNR; and (b2) ensuring thatthe areas identified in (b) are outside of the noise regions determinedin (b1).
 39. The method of claim 29 Wherein (b) comprises: (b1)determining high clutter regions of the image characterized by lowcoherence factor; and (b2) ensuring that the areas identified in (b) areoutside of the high clutter regions determined in (b1).
 40. The methodof claim 7 or 29 further comprising: suppressing noise in the inputsignal prior to (a).
 41. The method of claim 7 or 29 further comprising:suppressing noise in the statistical measure of variability.
 42. Themethod of claim 7 or 49 wherein (b) comprises: (b1) comparing thestatistical measure of variability of (a) against upper and lowerthresholds.
 43. The method of claim 42 wherein (b) further comprises:(b2) adjusting at least one of the upper and lower thresholds.
 44. In amedical ultrasonic imaging system, a method for adaptively controllinggain, said method comprising: (a) determining soft tissue averageamplitude at a plurality of locations of an image; (b) fitting a surfaceto the soft tissue average amplitude in the image; (c) adaptivelyvarying a gain of the system based at least in part on the surfacefitted in (b).
 45. The method of claim 44 wherein the surface is asecond order surface.
 46. The method of claim 44 wherein (c) comprises:(d) causing average amplitude of areas of soft tissue in the image to bedisplayed at substantially a target display value.
 47. The method ofclaim 44 wherein the surface is represented as a polynomial spline. 48.The method of claim 44 wherein the surface is represented as a linearcombination of a plurality of basis functions.
 49. In a medicalultrasonic imaging system, a method for adaptively controlling gain,said method comprising: (a) acquiring an image with a plurality ofacquisition parameters of the system set to respective pre-selectedvalues; (b) determining soft tissue average amplitude at a plurality oflocations of an image; (c) adaptively varying a gain of the system basedat least in part on the soft tissue average amplitude of (b).
 50. In amedical ultrasonic imaging system, a method for adaptively controllinggain, said method comprising: (a) determining soft tissue averageamplitude at a plurality of locations in an image; (b) determining noisevalues at said plurality of locations in the image; and (c) adaptivelyvarying a gain of the system based at least in part on the soft tissueaverage amplitude of (a) and the noise values of (b).
 51. The method ofclaim 50 wherein (c) is operative to cause average amplitude of softtissue to be displayed at substantially a target display value.
 52. Themethod of claim 50 wherein (c) comprises subtracting the noise valuesfrom the image over at least a portion of the image.
 53. The method ofclaim 50 wherein the noise values of (b) are indicative of spatial meansof amplitude-detected, log-compressed noise at respective locations inthe image.
 54. The method of claim 50 wherein the noise values of (b)are indicative of temporal means of amplitude-detected, log-compressednoise at respective locations in the image.
 55. The method of claim 50wherein the noise values of (b) are indicative of spatial variance ofnoise power normalized by local mean noise power.
 56. The method ofclaim 50 wherein the locations are arrayed in a one-dimensional arrayalong a range axis of the image.
 57. The method of claim 50 wherein thelocations are arrayed in a two-dimensional array along range andazimuthal axes of the image.
 58. The method of claim 7, 29 or 49 wherein(b) comprises identifying a spatial pattern in the statistical measureof (a) characteristic of a selected soft tissue.
 59. The method of claim7, 29 or 44 further comprising: (d) avoiding at least one signalprocessing operation from the following group during acquisition of theinput signal: frequency compounding, spatial compounding, spatialfiltering, temporal filtering.
 60. The method of claim 7, 29, 44 or 49wherein the imaging system displays an image, and wherein (b) isperformed for a region that extends beyond the image.
 61. In a medicalultrasonic imaging system, a method for adaptively controlling gain,said method comprising: (a) determining soft tissue average amplitude ata plurality of locations in an image; (b) determining noise values atsaid plurality of locations in the image; and (c) adaptively varyingdynamic range of the system based at least in part on the averageamplitude of (a) and the noise values of (b).
 62. The method of claim 61further comprising: (d) adaptively varying a gain of the system based atleast in part on the average amplitude of (a) and the noise values of(b).
 63. The method of claim 7, 29, 44, 49, 50 or 61 further comprising:(d) initiating (a), (b) and (c) in response to a user request.
 64. Themethod of claim 7, 29, 44, 49, 50 or 61 further comprising: (d)initiating (a), (b) and (c) automatically at a predetermined interval.65. The method of claim 7 or 58 wherein the spatial pattern comprises alinear region of reduced spatial variability.
 66. The method of claim 7,29, 44, 49, 50, or 61 further comprising: (d) initiating (a), (b), and(c) automatically in response to a substantial change in at least aportion of a frame generated by the imaging system.
 67. The method ofclaim 7, 29, 44, 49, 50, or 61 further comprising: (d) initiating (a),(b), and (c) automatically following a substantial increase followed bya substantial reduction of frame to frame change in at least a portionof a frame generated by the imaging system.
 68. The method of claim 7,29, 44, 49, 50, or 61 further comprising: (d) initiating (a), (b), and(c) automatically in response to a change in an imaging parameter of theimaging system.
 69. The method of claim 50 or 61 wherein the imagingsystem displays an image, and wherein (a) is performed for a region thatextends beyond the image.